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Algonomy DeepRecs vs. Constructor.io Search

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Algonomy DeepRecs
Score 0.0 out of 10
Enterprise companies (1,001+ employees)
DeepRecs makes recommendations for 'Similar Products' and ‘Complete the Look’ using product images and without manual merchandising. It leverages convolutional neural networks to detect and extract feature vectors and graph visual similarities between products. Further, DeepRecs helps shoppers discover new, seasonal, niche, and long-tail products—that otherwise remain buried due to lack of historical data—using NLP algorithms that leverage catalog descriptions and other textual data.N/A
Constructor.io Search
Score 7.9 out of 10
Enterprise companies (1,001+ employees)
Constructor Search promises to improve conversions and revenue from onsite and in-app search, using search science and artificial intelligence, Constructor's cloud-based search-as-a-service solution uses natural language processing, machine learning-enhanced results ranking, collaborative personalization, and merchant controls to power enterprise-grade onsite and in-app search. Whether search results are optimized for relevance, revenue, conversions, conversations — or all of the…N/A
Pricing
Algonomy DeepRecsConstructor.io Search
Editions & Modules
No answers on this topic
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Offerings
Pricing Offerings
Algonomy DeepRecsConstructor.io Search
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Community Pulse
Algonomy DeepRecsConstructor.io Search
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Algonomy DeepRecsConstructor.io Search
Small Businesses
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Medium-sized Companies
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User Ratings
Algonomy DeepRecsConstructor.io Search
Likelihood to Recommend
-
(0 ratings)
9.0
(1 ratings)
User Testimonials
Algonomy DeepRecsConstructor.io Search
Likelihood to Recommend
Algonomy (Manthan-RichRelevance)
No answers on this topic
Constructor.io Corporation
Constructor.io Search takes all of the guesswork out of maintaining a search engine. As merchandisers or product teams, we have educated guesses at how search relevance should work but the customer is always king. We can't always predict the ways in which consumers will search or what their intent is. That's why the behavioral-driven approach that Constructor.io employs works so well. It means that merchandisers can focus on their sales and promotional responsibilities, instead of wasting time and bandwidth on base-level relevance questions.
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Pros
Algonomy (Manthan-RichRelevance)
No answers on this topic
Constructor.io Corporation
  • Optimizes for business KPIs, not string matches, so merchandisers can focus on strategy instead of maintaining a dictionary.
  • Gives merchandisers control to curate the right customer experience per their expert understanding.
  • Customer/technical support during and after implementation.
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Cons
Algonomy (Manthan-RichRelevance)
No answers on this topic
Constructor.io Corporation
  • Visibility of the personalization algorithm.
  • Exposing detailed analytics within the customer-facing dashboard.
  • "Top searches" available via API.
Verified User
Anonymous
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